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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Summary


In this chapter, the time series examples used annual sunspot cycles data.

You learned that it's common to try to derive a relationship between a value and another data point or combination of data points a fixed number of periods in the past, in the same time series.

A moving average specifies a window of previously seen data, which is averaged each time the window slides forward by one period. In the pandas API, the rolling_window() function provides the window functions functionality with different values of the win_type string parameter corresponding to different window functions.

Cointegration is similar to correlation and is a metric to define the relatedness of two time series. In regression setups, we frequently encounter the problem of overfitting. This issue arises when we have a perfect fit for a sample, which performs poorly when we introduce new data points. To evaluate a model, we can compute appropriate evaluation metrics.

Databases are an important tool for data analysis...

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